Quantitative Information Fusion For Hydrological Sciences
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Quantitative Information Fusion for Hydrological Sciences
Author: Xing Cai
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-01-03
In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science’s future directions – namely, the quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences.
Quantitative Information Fusion for Hydrological Sciences
In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science’s future directions – namely, the quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences.
Deep Learning in Engineering, Energy and Finance
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